Fingerprinting Attack on Tor Anonymity using Deep Learning
Fingerprinting Attack on Tor Anonymity using Deep Learning
复制标题
DOI：
--
复制
发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Kotaro Abe;Shigeki Goto
中科院分区：
文献类型：
--
作者：
Kotaro Abe;Shigeki Goto
Tor是一种支持匿名通信的自由软件。它保护用户免受流量分析和网络监控的影响。它对保密的商业活动和国家安全也很有用。与此同时，匿名协议被用于访问犯罪网站，如那些处理非法毒品的网站。为了检测访问非法网站的用户，提出了一种利用指纹攻击对ToR流量进行分析的新方法。我们的新方法是基于堆叠去噪自动编码器，这是一种深度学习技术。我们的评估结果表明，在封闭世界测试中，准确率为0.88。在开放世界测试中，真阳性率为0.86，假阳性率为0.02。
Tor is free software that enables anonymous communication. It defends users against traffic analysis and network surveillance. It is also useful for confidential business activities and state security. At the same time, anonymized protocols have been used to access criminal websites such as those dealing with illegal drugs. This paper proposes a new method for launching a fingerprinting attack to analyze Tor traffic in order to detect users who access illegal websites. Our new method is based on Stacked Denoising Autoencoder, a deep-learning technology. Our evaluation results show 0.88 accuracy in a closed-world test. In an open-world test, the true positive rate is 0.86 and the false positive rate is 0.02.